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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Article Type: Other
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6893-6893, 2022
Authors: Batyrshin, Ildar | Gomide, Fernando | Kreinovich, Vladik | Shahbazova, Shahnaz
Article Type: Editorial
DOI: 10.3233/JIFS-219322
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6895-6896, 2022
Authors: Tavares, Emmanuel | Silva, Alisson Marques | Moita, Gray Farias
Article Type: Research Article
Abstract: Evolving models have shown great success in processing non-stationary data that change their characteristics over time. Motivated by elaborating a high-performance model for data classification, the present work proposes a new evolving fuzzy classifier. The proposed model, named evolving Fuzzy Mean Classifier (eFMC), has a low computational cost and is autonomous, i.e., no has user-defined parameters. The eFMC is based on fuzzy clustering structures, where the membership degree between the samples and the clusters is used to obtain the output. In the proposed approach, each class is represented by a cluster, and new clusters are created whenever a new class …is discovered. The centers of the clusters are updated through the sample’s means calculated incrementally. Computational experiments were carried out to evaluate and compare the performance of the eFMC in terms of accuracy and processing time. Experimental results and comparisons against alternative state-of-the-art evolving classifiers show that the eFMC is accurate and fast, characteristics essential for adaptive classifiers, especially in online and real-time environments. Show more
Keywords: Evolving systems, adaptive classifier, fuzzy systems, evolving fuzzy mean classifier
DOI: 10.3233/JIFS-212831
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6897-6908, 2022
Authors: Ekmekci, Dursun | Shahbazova, Shahnaz N.
Article Type: Research Article
Abstract: One of the most important issues for FLC systems is the problem of finding the right balance between interpretability and accuracy. For this delicate balance, several methods which can be integrated into fuzzy logic, and tune the fuzzy logic parameters adaptively, have been proposed. One of these popular approaches is the heuristic optimization method. However, in terms of optimization, designing fuzzy logic control is a complex optimization problem that is discrete in terms of rule optimization and numerical in terms of optimization of membership degrees parameters. In this context, heuristic-based adaptive fuzzy control systems focus on either fuzzy rule optimization, …weighting fuzzy rules, or parameter optimization. In this paper, unlike the others, an adaptive weighted fuzzy logic control (awFLC) method, which weights the inputs instead of the rules, is proposed. First, the membership degree of each input is calculated. Then, the resultant weight is determined by combining the weighted input membership degrees. For a crisp result, the average of the membership degrees of the resultant weight to the output membership functions is calculated. In awFLC, the interaction between membership functions is achieved by average membership degree, communication between inputs is achieved by the weighting of inputs, and mapping between inputs-outputs is achieved by the resultant weight value. Thus, the approach, which turns into a purely numerical optimization problem, provides convenience for heuristic search. In awFLC, optimal values for input weights and variable parameters are searched by the genetic algorithm. The performance of the method was tested on traction power control, and the results were compared with the ANFIS results. With awFLC, an 8.13% average error was obtained, while ANFIS produced solutions with an average error rate of 8.97%. Show more
Keywords: Adaptive fuzzy control, weighted fuzzy control, adaptive weighted fuzzy control
DOI: 10.3233/JIFS-220753
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6909-6916, 2022
Authors: Pokorádi, László | Kocak, Sinan | Tóth-Laufer, Edit
Article Type: Research Article
Abstract: Reliability and safety have always been the main focus while developing critical automotive systems such as brakes. Failure Modes and Effect Analysis (FMEA) is one of the primary systematic reliability analysis tools. Due to the arising uncertainties and subjectivity, in models such as this one the fuzzy approach is highly popular, i.e., the fuzzy rule-based risk assessment methods can be used to model and depict the subjective opinions of estimators mathematically. In this paper, the authors propose a methodological approach to the implementation of fuzzy rule-based Hierarchical FMEA (H-FMEA), where the membership functions are different depending on the layer. Moreover, …the information between each layer is transferred in the form of fuzzy numbers instead of crisp values, in order to further improve the reliability of the system. Show more
Keywords: Risk assessment, FMEA, hierarchical FMEA, fuzzy inference, wheel speed sensor
DOI: 10.3233/JIFS-212664
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6917-6923, 2022
Authors: Aguilar, Diego | Batyrshin, Ildar
Article Type: Research Article
Abstract: In recent years it has become popular to represent the foreign exchange market as a correlation network using the Pearson correlation coefficient as a measure of co-movement of exchange rates. We show that the Pearson correlation of financial time series could be misleading in analyzing their co-movements. We propose representing the co-movement of exchange rates as a non-directed graph using the measure of local trends associations (LTA). Each node in the graph represents a currency, and an edge between nodes represents an existing high association between currencies. We present several methods for network summary visualization showing the highest associations between …nodes. One method allows comparing graphs corresponding to different correlation and association measures. Another one is appropriate for comparing graphs using the same association measure. We present a dynamic analysis of association networks and the network of associations with a selected currency named a “node of interest.” We show that the currency networks based on LTA are better explainable than networks based on Pearson correlation. LTA based relationships between currencies better reflect geographical, economic or political relationships between corresponding countries. Show more
Keywords: Co-movement of financial time series, local trends association, FOREX network, time-series data mining
DOI: 10.3233/JIFS-220840
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6925-6932, 2022
Authors: Cortez, Solymar Ayala | Bokati, Laxman | Velasco, Aaron | Kreinovich, Vladik
Article Type: Research Article
Abstract: In many applications, including analysis of seismic signals, Daubechies wavelets perform much better than other families of wavelets. In this paper, we provide a possible theoretical explanation for the empirical success of Daubechies wavelets. Specifically, we show that these wavelets are optimal with respect to any optimality criterion that satisfies the natural properties of scale- and shift-invariance.
Keywords: Daubechais wavelets, seismology, invariance
DOI: 10.3233/JIFS-212021
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6933-6938, 2022
Authors: Almásy, Márton György | Hörömpő, András | Kiss, Dániel | Kertész, Gábor
Article Type: Research Article
Abstract: Revolutionary changes of deep reinforcement learning are leading to high-performing intelligent solutions in multiple fields, including healthcare. At the moment, chemotherapy and radiotherapy are common types of treatments for cancer, however, both therapies are usually radical procedures with undesirable side effects. There is an increasing number of evidence that patient-based optimal schedule has a significant impact in increasing efficiency and survival, and reducing side effects during both therapies. To apply artificial intelligence in therapy optimization, an adequate model of tumor growth incorporating the effect of the treatment is mandatory. A method on training a controller for dosage and scheduling, reinforcement …learning can be applied, where a well-chosen agent rewarding function is key to achieve optimal behavior. In this survey paper, some selected tumor growth models, reinforcement learning based solutions and especially agent reward functions are reviewed and compared, providing a summary on state of the art approaches. Show more
Keywords: Tumor growth models, reinforcement learning, reward functions, cancer therapy
DOI: 10.3233/JIFS-212351
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6939-6946, 2022
Authors: Contreras, Jonatan | Ceberio, Martine | Kosheleva, Olga | Kreinovich, Vladik
Article Type: Research Article
Abstract: Neural networks – specifically, deep neural networks – are, at present, the most effective machine learning techniques. There are reasonable explanations of why deep neural networks work better than traditional “shallow” ones, but the question remains: why neural networks in the first place? why not networks consisting of non-linear functions from some other family of functions? In this paper, we provide a possible theoretical answer to this question: namely, we show that of all families with the smallest possible number of parameters, families corresponding to neurons are indeed optimal – for all optimality criteria that satisfy some reasonable requirements: namely, …for all optimality criteria which are final and invariant with respect to coordinate changes, changes of measuring units, and similar linear transformations. Show more
Keywords: Neural networks, invariance, function approximation, theoretical explanation
DOI: 10.3233/JIFS-212009
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6947-6951, 2022
Authors: Hernández, Nayeli | Batyrshin, Ildar | Sidorov, Grigori
Article Type: Research Article
Abstract: Sentiment analysis is a task that belongs to natural language processing and it is highly used in texts extracted from social networks. This task consists of assigning the labels or classes: positive, negative or neutral to the text. However, analyzing a piece of text extracted from social networks to determine if it represents a positive or negative sentiment is a difficult task, because social media texts contain slangs, typographical errors and cultural context. The shortcomings of traditional frequency based feature extraction models such as bag of words or TF-IDF affect the accuracy of sentiment classification. To improve the precision in …the sentiment classification task, it is possible to use natural language modelling methods that are able to learn contextual information from words. In this work, word embedding such as Word2Vec, GloVe and Doc2VecC with different dimensions are used. The resulting word vectors will be used to train recurring neural networks such as LSTM, BiLSTM, GRU and BiGRU, to improve sentiment classification. Show more
Keywords: Sentiment analysis, natural language processing, deep learning, neural network, text classification
DOI: 10.3233/JIFS-211909
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6953-6963, 2022
Authors: Bochkarev, Vladimir V. | Maslennikova, Yulia S. | Shevlyakova, Anna V.
Article Type: Research Article
Abstract: In recent years, methods based on word embedding models have been widely used for solving problems of semantic change estimation. The models are trained on text corpora of various years. Semantic change is detected by analyzing changes in distance between words using vector space alignment or by analyzing changes in a set of words that are most similar in meaning to a target word. Testing for statistical significance of the detected effects has not been detailly discussed in previous studies. This paper focuses on the problem of testing statistical significance of semantic change. Besides, we consider the problem of finding …a confidence interval of estimates of semantic distance between words. We allow for the influence of two random factors. The first one is associated with the use of random initial conditions and stochastic optimization when training the model, the second one results from a random selection of texts for a training corpus. The proposed approach is based on the use of resampling of a training set of texts. The proposed method is tested on the COHA corpus. Show more
Keywords: Semantic change, word embedding, bootstrapping, Corpus of Historical American English
DOI: 10.3233/JIFS-212179
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6965-6977, 2022
Authors: Rico-Preciado, Erick | Laureano, Mayte H. | Calvo, Hiram
Article Type: Research Article
Abstract: Learning relationships between nodes in a directed graph is a task that has been widely studied and it has been applied to a large number of topics and research areas. We establish a definition of particular kind of relationship, called analogy in a directed multigraph. An analogy can be defined for a certain pair of concepts, and the paths connecting them are called explanation of this analogy. We experiment with a structure built from real oneiric stories obtained from psychoanalytic descriptions (e.g. mother is represented as a bull; book represents power). Analogies found by the analysts are automatically identified by …means of linguistically motivated patterns. Analogies have degrees of similarity based on the words used to describe them: represents, is a, is like a, can be a, refers to, etc. Once they are identified and graded, they are represented in the multidigraph, allowing us to provide different hypotheses in how these analogies can be explained. In order to enrich the concept graph, we added information from ConceptNet and WordNet. In addition, we propose a learning method for association rules that, given the degree of the analogy and a starting concept, allow reaching a destination concept. For example, starting from “dream”, we obtain the path <dream, psychic, neurosis, symptom>, being "dream is a symptom" a description previously given by a psychoanalyst, that was not included when training the algorithm. We evaluated 100 analogies on 171 concepts with 8,034 properties using Leave One Out cross validation, and found that the correct analogy was found within the all the possible paths for 94% of the analogies, restricted to 85% if only the top 20% possible paths are considered. This implies that, by using our method, it is possible to learn analogies between two concepts by reconstructing paths of different lengths based on local decisions considering concept, property and degree of analogy. Show more
Keywords: Directed graphs, analogy, concept representation, explainable artificial intelligence, psychoanalysis
DOI: 10.3233/JIFS-211895
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6979-6994, 2022
Authors: Balouchzahi, Fazlourrahman | Shashirekha, Hosahalli Lakshmaiah | Sidorov, Grigori | Gelbukh, Alexander
Article Type: Research Article
Abstract: Curfews and lockdowns around the world in the Covid-19 era have increased the usage of the internet drastically and accordingly the amount of data shared on social media. In addition to using social media for sharing useful information, some miscreants are using the power of social media to spread hate speech and offensive content. Filtering the offensive language content manually is a laborious task due to the huge volume of data. Further, rapid developments in hardware and software technology have provided opportunities for users to post their comments not only in English but also in their native language scripts. However, …based on the ease of Roman script usage, social media users specifically in multilingual countries like India, prefer to comment in code-mixed and multi-script texts. The typical systems that are employed to process and analyze monolingual texts are usually not appropriate for these kinds of texts. Further, as these texts do not adhere to the rules and regulations of any language to frame the words and sentences, the complexity of analyzing such texts increases. The novelty of the present study is to address the Offensive Language Identification (OLI) task in code-mixed and multi-script texts, this paper proposes to use relevant syllable and character n-grams features to train Machine Learning (ML) classifiers. The performance of the proposed models is evaluated on three Dravidian language pairs, namely: Malayalam-English, Tamil-English, and Kannada-English. The performances of ML classifiers prove the effectiveness of syllable and character n-grams features for code-mixed and multi-script texts analysis. Show more
Keywords: Code-mixed, multi-script, offensive language identification, syllable, character n-grams
DOI: 10.3233/JIFS-212872
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6995-7005, 2022
Authors: Zhang, Ming | Du, Qian | Yang, Jianxun | Liu, Song
Article Type: Research Article
Abstract: The Pile movement is one of the most crucial matters in designing piles and foundations that need to be estimated for any project failure. Over the variables used in forecasting Pile Settlement, many methods have been introduced to appraise it. However, existing a wide range of theoretical strategies to investigate the pile subsidence, the soil-pile interactions are still ambiguous for academic researchers. Most studies have tried to work out the subsidence rate in piles after loading passing time by artificial intelligence methods. Generally, the Artificial Neural Network (ANN) has drawn attention to show the actual views of pile settlement over …the loading phase vertically. This research aims to present the Hybrid Radial Basis Function neural network integrated with the Novel Arithmetic Optimization Algorithm and Biogeography-Based Optimization to calculate the optimal number of neurons embedded in hidden layers. The transportation network of Klang Valley, Mass Rapid Transit in Kuala Lumpur, Malaysia, was chosen to analyze the piles’ settlement and earth features using HRBF-AOA and HRBF-BBO scenarios. Over the prediction process, the R-values of HRBF-AOA and HRBF-BBO were obtained at 0.9825 and 0.9724, respectively. The MAE also shows a similar trend as 0.2837 and 0.323, respectively. Show more
Keywords: Pile in rock, settlement, prediction, radial basis function, biogeography-based optimization, arithmetic optimization algorithm, r-value correlation
DOI: 10.3233/JIFS-221021
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7009-7022, 2022
Authors: Dharaniya, R. | Indumathi, J. | Uma, G.V.
Article Type: Research Article
Abstract: Text generation is one of the complex tasks associated with natural language processing. For efficient text generation, syntax and semantics of the language have to be considered to assign context to key phrases. The main objective of the proposed work is to perform text generation specifically for movie scripts. The training data consist of a self-annotated corpus of movie scripts depicting scenes, specific to certain genre where the annotation mainly focuses on a specific director’s movie scripts. The scene generation is set forth by word embedding with sentiment classification where the emotionally analyzed words are vectorized using the EmoVec algorithm …performing sentiment analysis. Based on the sentiment and location associated with each scene, context for the phrases are identified and proceeded to build a well-defined script. Bidirectional Long Short-Term Memory BLSTM with multi-head Attention is used to capture the information processed in both forward and backward propagation in order to understand future context. The vocabulary is built using Stanford’s Internet Movie Database IMDB datasets to perform word based encoding for which requirement of an extensive vocabulary is imminent. Show more
Keywords: Intelligent system, semantic computing, long short-term memory, natural language processing, recurrent neural network
DOI: 10.3233/JIFS-212271
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7023-7039, 2022
Authors: Li, Yueen | Feng, Qi | Huang, Tao | Wang, Shennan | Cong, Weifeng | Knighton, Edwin
Article Type: Research Article
Abstract: The Artificial Neural Networks (ANN) are more widely used in the New Product Development (NPD) process in recent years. The product data generation process is a prerequisite for the application of the ANN algorithm. In the development of new products, the Kansei Engineering (KE) method is an effective emotion-based data generation method. The Semantic Difference (SD) method is usually used to obtain data to apply to design idea generation. Facing the data demand of product creativity, it is important to establish the relationship between consumer perception and product expression. Numerical relationships are not linear and several methods are required for …solving these problems. The method of the Back Propagation (BP) neural network is simple and effective to be used in this case. This paper proposes an innovative data modeling method using digital coding and KE. This model explores a rational design method of perceptual intention and builds an intelligent model. Compared with traditional method, the modified model can quickly and accurately reflect the users’ perceptual needs, make the design more scientific, improve the design efficiency, and reduce design costs. This method is used in the design of electric welding machines, and this process can effectively provide technical support for NPD process in small and medium-sized enterprises. Show more
Keywords: New product development, KE, semantic difference, ANN, BP
DOI: 10.3233/JIFS-212441
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7041-7055, 2022
Authors: Chen, Shuang | Ren, Tao | Qv, Ying | Shi, Yang
Article Type: Research Article
Abstract: Dealing with the explosive growth of web sources on the Internet requires the use of efficient systems. Automatic text summarization is capable of addressing this issue. Recent years have seen remarkable success in the use of graph theory on text extractive summarization. However, the understanding of why and how they perform so well is still not clear. In this paper, we intend to seek a better understanding of graph models, which can benefit from graph extractive summarization. Additionally, analysis has been performed qualitatively with the graph models in the design of recent graph extractive summarization. Based on the knowledge acquired …from the survey, our work could provide more clues for future research on extractive summarization. Show more
Keywords: Text summarization, extractive summarization, graph theory, extraction scheme, sentiment analysis
DOI: 10.3233/JIFS-220433
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7057-7065, 2022
Authors: Elanangai, V. | Vasanth, K.
Article Type: Research Article
Abstract: In today’s world, Steel plates play essential materials for various industries like the national defense industry, chemical industry, automobile industry, machinery manufacturing, etc. However, some defects may occur in a few plates during the manufacture of stainless-steel plates which directly impact the quality of the stainless-steel plate. If the faulted plate detection can be done manually, then it leads to errors and a time-consuming process. Hence, a computerized automated system is necessary to detect the abnormalities. In this paper, a novel Adaptive Faster Region Convolutional Neural Networks (AFRCNN) scheme has been proposed for automatic fault detection of stainless-steel plates. The …proposed AFRCNN scheme comprises three phases: identification, detection, and recognition. Primarily, the damaged plates are identified using Region Proposal Network and Fully Convolutional Neural Network functioning as a combined process under AFRCNN. In the next phase, the number corresponding to the particular plate is recognized through the standard Automated Plate Number Recognition approach with the support of the character recognition technique. The simulation results manifest that the proposed AFRCNN scheme obtains a superior classification accuracy of 99.36%, specificity of 99.24%, and F1-score of 98.18% as compared with the existing state-of-the-art schemes. Show more
Keywords: Fault detection, stainless steel plates, convolutional neural network, classification, region proposal network
DOI: 10.3233/JIFS-213031
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7067-7079, 2022
Authors: Alqhtani, Samar M.
Article Type: Research Article
Abstract: Disasters occur due to naturally stirring events like earthquake, floods, tsunamis, storms hurricanes, wildfire, and other geologic measures. Social media fake image posting influence is increasing day by day regarding the natural disasters. A natural disaster can result in the death or destruction of property, as well as economic damage, the severity of which is determined by the resilience of the affected population and the infrastructure available. Many researchers applied different machine learning approaches to detect and classification of natural disaster types, but these algorithms fail to identify fake labelling occurs on disaster events images. Furthermore, when many natural disaster …events occur at a time then these systems couldn’t handle the classification process and fake labelling of images. Therefore, to tackle this problem I have proposed a FLIDND-MCN: Fake Label Image Detection of Natural Disaster types with Multi Model Convolutional Neural Network for multi-phormic natural disastrous events. The main purpose of this model is to provide accurate information regarding the multi-phormic natural disastrous events for emergency response decision making for a particular disaster. The proposed approach consists of multi models’ convolutional neural network (MMCNN) architecture. The dataset used for this purpose is publicly available and consists of 4,428 images of different natural disaster events. The evaluation of proposed model is measured in the terms of different statistical values such as sensitivity, specificity, accuracy, precision, and f1-score. The proposed model shows the accuracy value of 0.93 percent for fake label disastrous images detection which is higher as compared to the already proposed state-of-the-art models. Show more
Keywords: Convolutional neural network, fake labeling, natural disaster, image classification
DOI: 10.3233/JIFS-213308
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7081-7095, 2022
Authors: Kavitha, P. | Latha, L. | Palaniswamy, Thangam
Article Type: Research Article
Abstract: Big Data is a popular research area where a vast amount of data is created, replicated, and consumed by society. The quality of the data used directly influences big data knowledge discovery. The existence of noise is the most prevalent problem influencing data quality. The following techniques were developed to reduce noise in data with a distributed setting: Homogenous Ensemble for Big Data (HME-BD) and Heterogeneous Ensemble for Big Data (HTE-BD). In this article, the performance of HTE-BD is improved further by developing Enhanced HTE-BD (EHTE-BD), which combines Logistic Regression based Support Vector Machine (LR-SVM) in conjunction with RF, LR, …and KNN to reduce noisy data. Furthermore, the Multi-Objective Evolutionary Fuzzy Method for Subgroup Discovery throughout Big Data (MEFASD-BD) was used to resolve the multi-objective optimization challenge, and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was utilized to handle the rising dimensionality issue through subgroup discovery. To address the NSGA-II’s slow convergence rate, an Improved Multi-Objective Meta-Heuristic Fuzzy approach for discovering subgroups in big data is described, that contains a meta-heuristic method for subgroup discovery known as the Multi-Objective Differential Search Algorithm (MODSA). It selects the most relevant subgroups from vast amounts of data, reducing the data’s dimensionality. The Fuzzy Deep Neural Network (FDNN) classifier assesses the main subgroups. By removing noisy data and selecting the most relevant subgroups, the performance of FDNN in classifying vast amounts of data is improved. Show more
Keywords: Big data analysis, logistic regression-based support vector machine, multi-objective differential search algorithm, fuzzy deep neural network, random forest, high dimensionality problem, subgroup discovery, slow convergence rate
DOI: 10.3233/JIFS-220171
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7097-7113, 2022
Authors: Ponmalar, S. Joshibha | Prasad, Valsalal | Kannadasan, Raju
Article Type: Research Article
Abstract: A novel technique is presented for Maximum Power Point Tracking (MPPT) based photovoltaic (PV) system in partial shadow conditions for harvesting maximum power. In this paper, a hybrid technique is developed, which combines Black Widow Optimization (BWO) with Recurrent Neural Network (RNN). To train the data set and provide a control signal for the converter, an RNN is used. After fitting the training data sets, the suggested method achieved maximum power by utilizing BWO based on the control parameters. This proposed method minimizes the difference between actual and average power. Using an optimization technique, the main goal of this proposed …strategy is to obtain peak power harvest under various conditions, including partial shading, while minimizing error function, With the help of MATLAB/Simulink software, the conclusions are revealed under various partial shading conditions. For each category, the observed results are evaluated at various time intervals. The proposed method is also compared to other techniques such as the Ant Colony Optimization (ACO)-RNN system, Particle Swarm Optimization (PSO)-RNN system, and Gravitational Search Algorithm (GSA)-RNN system. The proposed system is 36.11% faster than GSA with RNN, 39.47% faster than PSO, and 42.5% faster than ACO with RNN in terms of tracking speed. Significantly, the proposed work is 0.87% more efficient than the other models in terms of obtaining maximum power. In terms of obtaining maximum power, the proposed work BWOA-RNN is more effective than other methods. Show more
Keywords: Partial shading, maximum power point tracking (MPPT), photovoltaic (PV), black widow optimization (BWO), recurrent neural network (RNN), gravitational search algorithm (GSA), ant colony optimization (ACO), and particle swarm optimization (PSO)
DOI: 10.3233/JIFS-220892
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7115-7133, 2022
Authors: Li, Wenfeng | Deng, Xiaoping | Wang, Ruiqi | Meng, Songping
Article Type: Research Article
Abstract: Energy or load disaggregation, as one essential part of non-intrusive load monitoring (NILM), is an efficient way to separate the consumption information of target appliances from the whole consumption data, and can accordingly help to regulate people’s energy consumption behaviors. However, the consumptions of the target appliances are usually affected by the variance of the opening time, working condition and user interference, so it is a difficult task to realize precise disaggregation. To further improve the energy disaggregation accuracy, this paper proposes a new parallel disaggregation strategy with two subnets for the energy consumption disaggregation of the target appliances in …the residential buildings. In the proposed strategy, the parallel disaggregation network contains a long-term disaggregation network and a short-term disaggregation network, which can automatically and respectively learn the long-term trend features and short-term dynamic characteristics of the electrical appliances. This parallel structure can make full use of the advantages of different methods in feature extraction, so as to model the appliance features more comprehensively. To better extract the long-term and short-term features, in the long-term disaggregation subnet, we propose the double branch bi-directional temporal convolution network (DBB-TCN) which has a wider receptive field than the traditional temporal convolution networks (TCN), while in the short-term disaggregation subnet, we adopt the convolution auto-encoder to learn the short-term characteristics of the target appliances. Finally, detailed experiments and comparisons are made with two real-world datasets. Experimental results verified that the proposed parallel disaggregation method performs better than the existing methods under various evaluation criteria. Show more
Keywords: Non-intrusive load monitoring, energy disaggregation, deep learning, temporal convolution network, auto-encoder
DOI: 10.3233/JIFS-212679
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7135-7151, 2022
Authors: Ubale Kiru, Muhammad | Belaton, Bahari | Chew, Xinying | Almotairi, Khaled H. | Hussein, Ahmad MohdAziz | Aminu, Maryam
Article Type: Research Article
Abstract: One of the fastest-growing fields in today’s world is data analytics. Data analytics paved the way for a significant number of research and development in various fields including medicine and vaccine development, DNA analysis, artificial intelligence and many more. Data plays a very important role in providing the required results and helps in making critical decisions and predictions. However, ethical and legislative restrictions sometimes make it difficult for scientists to acquire data. For example, during the COVID-19 pandemic, data was very limited due to privacy and regulatory issues. To address data unavailability, data scientists usually leverage machine learning algorithms such …as Generative Adversarial Networks (GAN) to augment data from existing samples. Today, there are over 450 algorithms that are designed to re-generate or augment data in case of unavailability of the data. With many algorithms in the market, it is practically impossible to predict which algorithm best fits the problem in question, unless many algorithms are tested. In this study, we select the most common types of GAN algorithms available for image augmentation to generate samples capable of representing a whole data distribution. To test the selected models, we used two unique datasets, namely COVID-19 CT images and COVID-19 X-Ray images. Five different GAN algorithms, namely CGAN, DCGAN, f-GAN, WGAN, and CycleGAN, were selected and applied to the samples to see how each algorithm reacts to the samples. To evaluate their performances, Visual Turing Test (VTT) and Fréchet Inception Distance (FID) were used. The VTT result shows that a human expert can accurately distinguish between different samples that were produced. Hence, CycleGAN scored 80% in CT image dataset and 77% in X-Ray image dataset. In contrast, the FID result revealed that CycleGAN had a high convergence and therefore generated high quality and clearer images on both datasets compared to CGAN, DCGAN, f-GAN, and WGAN. This study concluded that the CycleGAN model is the best when it comes to image augmentation due to its friendliness and high convergence. Show more
Keywords: Generative adversarial networks, CGAN, DCGAN, f-GAN, WGAN, CycleGAN
DOI: 10.3233/JIFS-220017
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7153-7172, 2022
Authors: Li, Song | Wang, Jie-Sheng | Song, Hao-Ming | Zheng, Yue | Zhang, Xing-Yue
Article Type: Research Article
Abstract: Archimedes optimization algorithm (AOA) is a metaheuristic algorithm inspired by the Archimedes physical law. It simulates the principle of buoyancy applied upward to partially or completely submerged objects. The decay energy of buoyancy, Lévy flight and Tangent flight are introduced into AOA. The buoyancy energy is adopted as the judgment condition of global search and local search. Then the location updating methods based on Lévy flight and Tangent flight are proposed so as to enhance its ergodicity and unrepeatable, improve the convergence speed and accuracy. Finally, through a large number of simulation experiments on 25 benchmark functions in CEC-BC-2017, the …improved AOAs are compared to show their advantages and disadvantages. On the other hand, two engineering design problems (pressure vessel design and spring design problem) are optimized. The experimental results show that the AOA based on buoyancy energy mixed Lévy flight and Tangent flight can solve the function optimization and engineering optimization problems well. It has the strong balance between exploration and exploitation, fast convergence speed and high search accuracy. Show more
Keywords: Archimedes optimization algorithm, buoyancy energy, Lévy flight, tangent flight
DOI: 10.3233/JIFS-221039
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7173-7197, 2022
Authors: Monikandan, A.S. | Agees Kumar, C.
Article Type: Research Article
Abstract: In this research, UPQC (Unified Power Quality Conditioner) with optimized hybrid fuzzy controller based GBSSA (Gaussian Barebone Salp Swarm Algorithm) with EPLL (Enhanced Phase Locked Loop) have been proposed for power quality enhancement in power distribution networks. Using the proposed method, the difficulties in major of the power distribution system networks can be solved, related to power quality issues. GBSSA has been employed in this research, to improve solution accuracy and optimization efficiency. Given that, it is permissible to add some extra time cost to acquire a better solution, based on the Non-Free Lunch (NFL) theory, and that the time …consumption of function evaluation is rather large, when addressing actual optimization problems, the extra time consumption can be overlooked to some extent. The EPLL control method improves the standard PLL, by reducing its fundamental flaw, which is the occurrence of main frequency errors, as well as double frequency errors. It controls the DC-bus voltage of unified power quality conditioners, during supply voltage and load voltage turbulences. The proposed UPQC control technique has been found to be resilient, to a variety of source and load perturbations, including unbalanced, transient distorted supply, voltage sag, unbalanced load and voltage swell. The proposed optimized GBSSA hybrid fuzzy controller with EPLL has been proven to be more effective in reducing the THD (Total Harmonic Distortion) to 3.22%. Moreover, comparative analysis with a conventional TSF-PLL has been performed with that of Takagi-Sugeno fuzzy controller and implemented using MATLAB (MATrix Laboratory). Show more
Keywords: UPQC, enhanced PLL, GBSSA, hybrid fuzzy controller, power quality issues
DOI: 10.3233/JIFS-213263
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7199-7211, 2022
Authors: Hu, Wujin | Li, Bo | Chen, Likang
Article Type: Research Article
Abstract: Physical Health is an important part of health education and health promotion in my country, and the health literacy level of students majoring in physical education in colleges and universities is an important factor in the development of health education in primary and secondary schools, and also directly affects the implementation of school health education in the future. The physical health evaluation of College students is frequently viewed as the multiple attribute decision making (MADM) issue. In our article, we combine the geometric Heronian mean (GHM) operator, generalized weighted Heronian mean (GWHM) operator with 2-tuple linguistic neutrosophic numbers (2TLNNs) to …propose the generalized 2-tuple linguistic neutrosophic geometric HM (G2TLNGHM) operator and generalized 2-tuple linguistic neutrosophic weighted geometric HM (G2TLNWGHM) operator. Meanwhile, some ideal properties of built operator are studied. Then, the G2TLNWGHM operator is applied to deal with the MADM problems under 2TLNNs. Finally, an example for Physical health evaluation of College students is used to show the proposed methods. Show more
Keywords: Multiple attribute decision making (MADM), neutrosophic numbers, 2-tuple linguistic neutrosophic numbers set (2TLNNSs), G2TLNGHM operator, G2TLNWGHM operator, physical health evaluation of College students
DOI: 10.3233/JIFS-221684
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7213-7225, 2022
Authors: Mahfouz, Mohamed A.
Article Type: Research Article
Abstract: The required division and exponentiation operations needed per iteration for the possibilistic c-means (PCM) clustering algorithm complicate its implementation, especially on homomorphically-encrypted data. This paper presents a novel efficient soft clustering algorithm based on the possibilistic paradigm, termed SPCM. It aims at easing future applications of PCM to encrypted data. It reduces the required exponentiation and division operations at each iteration by restricting the membership values to an ordered set of discrete values in [0,1], resulting in a better performance in terms of runtime and several other performance indices. At each iteration, distances to the new clusters’ centers are determined, …then the distances are compared to the initially computed and dynamically updated range of values, that divide the entire range of distances associated with each cluster center into intervals (bins), to assign appropriate soft memberships to objects. The required number of comparisons is O(log the number of discretization levels). Thus, the computation of centers and memberships is greatly simplified during execution. Also, the use of discrete values for memberships allows soft modification (increment or decrement) of the soft memberships of identified outliers and core objects instead of rough modification (setting to zero or one) in related algorithms. Experimental results on synthetic and standard test data sets verified the efficiency and effectiveness of the proposed algorithm. The average percent of the achieved reduction in runtime is 35% and the average percent of the achieved increase in v-measure, adjusted mutual information, and adjusted rand index is 6% on five datasets compared to PCM. The larger the dataset, the higher the reduction in runtime. Also, SPCM achieved a comparable performance with less computational complexity compared to variants of related algorithms. Show more
Keywords: Clustering algorithms, fuzzy clustering, possibilistic c-means, hybrid soft clustering, homomorphic encryption
DOI: 10.3233/JIFS-213172
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7227-7241, 2022
Authors: Thessalonica, D. Juliet | Khanna Nehemiah, H. | Sreejith, S. | Kannan, A.
Article Type: Research Article
Abstract: Software developers find it difficult to select the specific detection rules for different smell types. A set of metrics, thresholds and labels constitutes a code smells detection rule. The generated rules must be optimized efficiently to ensure successful rule selection. The objective is to identify how rules are generated from the labeled data set and selected using bio-inspired algorithms. The goals are met by employing the C4.5 and RIPPER algorithms to generate rules then, optimized using two bio-inspired algorithms, the Salp Swarm Algorithm (SSA) and Cockroach Swarm Optimization (CSO). The optimized sets of rules are evaluated using the similarity metrics …which are computed with the help of expected and the detected code smells. The common rule subsets from SSA and CSO are merged to produce the optimal rule subset which can be used for code smell detection. The proposed work has been experimented on Xerces-J, Log4J, Gantt Project and JFreeChart dataset. The work detected code smells with an accuracy of 91.7% for Xerces-J, 96.7% for JFreeChart, 88.6% for Gantt Project and 98% for Log4J. The findings will be useful for both theory and research since the proposed framework allows focusing on rule selection. Show more
Keywords: Software metric, code smell, Salp Swarm Algorithm, Cockroach Swarm Optimization
DOI: 10.3233/JIFS-220474
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7243-7260, 2022
Authors: Wang, Zeyuan | Wei, Guiwu | Guo, Yanfeng
Article Type: Research Article
Abstract: The main research of this paper is decision making under the dual probabilistic linguistic term sets (DPLTSs). This paper introduces a method, which combined TODIM method and CRITIC method. In this research, the CRITIC method is used to determine the weight, and the distance formula of TODIM method has been modified in order to adapt to DPLTS situation. Then, the TODIM method is used for multi-attribute group decision making (MAGDM) problem. Finally, a case study concerning investment project selection is given to demonstrate the merits of the developed methods. This combined method can be used for the automatic areal feature …matching, medical quality assessment, and ranking of matching processes. There are very few papers about using TODIM method under DPLTS situation at present, so this is a new perspective on MAGDM. The DPLTS-TODIM-CRITIC method was compared with correlation coefficient method and closeness coefficient method, and it is easy to find the advantage of this new method over the other two existing methods. Show more
Keywords: Multi-attribute group decision making (MAGDM), dual probabilistic linguistic term set, TODIM, CRITIC, Generalized normalized distance measure; investment project selection
DOI: 10.3233/JIFS-220502
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7261-7276, 2022
Authors: Agyemang, Isaac Osei | Zhang, Xiaoling | Adjei-Mensah, Isaac | Agbley, Bless Lord Y. | Mawuli, Bernard Cobbinah | Fiasam, Linda Delali | Sey, Collins
Article Type: Research Article
Abstract: Waypoints have enhanced the prospect of fully autonomous drone applications. However, Geographical Position System (GPS) spoofing and signal interferences are key issues in waypoint-based drone applications. Also, conceptual waypoint-based drone applications require accurate awareness of waypoints based on environmental cues and integration of additional sensing modalities. Additional sensor modalities may overwhelm drones’ processing resources, reducing operational time. This study proposes W-MobileNet, a denoising model for autonomous trajectory trail navigation based on precision control of a path planner, denoising capabilities of Weiner filters, and perceptual knowledge of convolutional neural networks. Creatively integrating the modules of W-MobileNet results in an intuitive drone …navigation controller characterized by position, orientation, and speed estimation. Further, a generic loss function that significantly aids models to converge faster during training is proposed based on adaptive weights. An extensive evaluation of a simulated and real-world experiment shows that W-MobileNet is more favorable in precision and robustness than contemporary state-of-the-art models. W-MobileNet has the potential to become one of the standards for autonomous drone applications. Show more
Keywords: Navigation, waypoint, drone, unmanned aerial vehicle, autonomous, deep convolutional neural network
DOI: 10.3233/JIFS-220693
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7277-7295, 2022
Authors: Tian, Xianghua | Luan, Feng | Li, Xu | Wu, Yan | Chen, Nan
Article Type: Research Article
Abstract: In the hot strip rolling process, accurate prediction of bending force is beneficial to improve the accuracy of strip crown and flatness, and further improve the strip shape quality. Due to outliers and noise are commonly present in the data generated in the rolling process, not only the prediction accuracy should be considered, but also the uncertainty of prediction results should be described quantitatively. Therefore, for the first time, the authors establish an interval prediction model for bending force in hot strip rolling process. In this paper, we use Artificial Neural Network (ANN) and whale optimization algorithm (WOA) to produce …a prediction interval model (WOA-ANN) for bending force in hot strip rolling. Based on the point prediction by ANN, interval prediction is completed by using lower upper bound estimation (LUBE) and WOA, and three indexes are used to evaluate the performance of the model. This paper uses real world data from steel factory to determine the optimal network structure and parameters of the interval prediction model. Furthermore, the proposed WOA-ANN model is compared with other interval prediction models established by other three optimization algorithms. The experimental results show that the proposed WOA-ANN model has high reliability and narrow interval width, and can well complete the interval prediction of bending force in hot strip rolling. This study provides a more detailed and rigorous basis for setting bending force in hot strip rolling process. Show more
Keywords: Artificial neural network (ANN), whale optimization algorithm (WOA), bending force, lower upper bound estimation (LUBE), interval prediction
DOI: 10.3233/JIFS-221338
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7297-7315, 2022
Authors: Qi, Quan-Song
Article Type: Research Article
Abstract: The performance evaluation of public charging service quality is frequently viewed as the multiple attribute group decision-making (MAGDM) issue. In this paper, an extended TOPSIS model is established to provide a new means to solve the performance evaluation of public charging service quality. The TOPSIS method integrated with FUCOM method in probabilistic hesitant fuzzy circumstance is applied to rank the optional alternatives and a numerical example for performance evaluation of public charging service quality is used to test the newly proposed method’s practicability with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple …to compute. The main results of this paper: (1) A novel PHF-TOPSIS method is proposed; (2) The extended TOPSIS method is developed in the probabilistic hesitant fuzzy environment; (3) The FUCOM method is used to obtain the attribute weight; (4) The normalization process of the original data has adapted the latest method to verify the precision; (5) The built models and methods are useful for other selection issues and evaluation issues. Show more
Keywords: Multiple attributes group decision making (MAGDM), probabilistic hesitant fuzzy sets (PHFS), FUCOM method, TOPSIS method, performance evaluation, public charging service quality
DOI: 10.3233/JIFS-220999
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7317-7328, 2022
Authors: Karthik, G.L. | Samson Ravindran, R.
Article Type: Research Article
Abstract: Fetal Electrocardiogram (FECG) analysis helps in diagnosis of fetal heart. Extracting FECG from composite abdominal signal that contains noises like maternal ECG (MECG), electrical interference etc is a topic of great research interest, and several approaches have been reported. The proposed method is Heuristic RNN-based Kalman Filter for Fetal Electrocardiogram Extraction (HRKFFEE) which is based on redundant noise and signal patterns in the residual signal of FECG and MECG. Two functional blocks are used in the proposed method. The first functional block is based on Heuristic RNN equipped with legacy Long Short-Term Memory (LSTM) for assembling a knowledgebase and the …second functional block is RNN-based Kalman filter. Upon testing, the proposed method delivers better average values of accuracy, F Score, Precision and Specificity as 93.118%, 93.106%, 92.9495 % and 92.98% respectively. Show more
Keywords: FECG Extraction, RNN-based Kalman filter, Legacy LSTM
DOI: 10.3233/JIFS-221549
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7329-7340, 2022
Authors: Zhang, Guidong | Sheng, Yuhong | Shi, Yuxin
Article Type: Research Article
Abstract: The multivariate uncertain regression model reveals the relationship between the explanatory and response variables to us very effectively. In this paper, firstly, the uncertain maximum likelihood estimation method for the parameters of the one-dimensional uncertain regression model is extended to the multivariate uncertain regression model to obtain estimates of the parameters. Secondly, in order to determine the reasonableness of the estimated values that are obtained by the various parameter estimation methods, uncertain hypothesis testing is applied to the multivariate uncertain regression model. Finally, some numerical examples are given to verify the feasibility of the method.
Keywords: Multivariate uncertain regression model, maximum likelihood estimation, uncertain hypothesis testing
DOI: 10.3233/JIFS-213322
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7341-7350, 2022
Authors: Bakhat, Khush | Kifayat, Kashif | Islam, M. Shujah | Islam, M. Mattah
Article Type: Research Article
Abstract: The method of marking video clips with action symbols is known as vision-based human activity recognition. Robust solutions to this problem have a variety of practical implementations. Due to differences in motion performance, recording environments, and inter-personal differences, the challenge is difficult. We specifically resolve these problems in this study work, and we solve imitations of state-of-the-art research. Projected human activity recognition is based on an amalgamation of CEV & SGM features. The proposed solution outperforms current models and produces state-of-the-art outcomes as compared to the best effectiveness of the control, according to experimental results on the datasets.
Keywords: Complex networks, entropy, human activity recognition, human action recognition, CEV, SGM
DOI: 10.3233/JIFS-213514
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7351-7362, 2022
Authors: Pavithra, P. | Hariharan, B.
Article Type: Research Article
Abstract: Cloud computing become increasingly more famous consistently, as numerous associations tend to outsource their information. With the outbreak of email message leakage, the protection and security of sensitive email data have become clients’ essential concerns. Encrypted email data is a superior method to ensure security, yet it will enormously restrict the searching. To take care of this difficulty keyword-based search over encrypted information is presented. The current search strategies permit the client to search utilizing just the specific keywords. There is no capacity to bear errors and format irregularities. In order to overcome those drawbacks, optimal secured fuzzy-based multi-keyword search …over encrypted email data is proposed here. The email sender encrypts the email data before outsourcing the data to a cloud server. For encryption, the proposed method utilizes the optimal secure XOR (OSXOR) encryption algorithm. Here the key value is optimally selected by the mayfly optimization algorithm (MOA). After the encryption, the encrypted email is outsourced to the cloud server. The data owner creates an encrypted searchable index using an input file to enable querying across encrypted emails and then assigns either the index or the gathering of encrypted messages to a cloud server. The receiver receives them from the cloud server and is fed back information, but it is unable to comprehend the signal. The recipient of the encrypted email can decode it and create a search trapdoor in the encrypted email database. For searching, fuzzy-based multi-keyword search is proposed. The effectiveness of the proposed methodology is analyzed in terms of different metrics namely, Memory Usage, Execution time, Encryption and decryption time and search time. The experimental result shows that the proposed method takes a minimum search time is 0.51 s and it achieves maximum searching accuracy of 98%. The implementation is done in JAVA with a Cloud simulator. Show more
Keywords: Cloud computing, encryption, decryption, mayfly optimization, fuzzy-based multi keyword search and search time
DOI: 10.3233/JIFS-213521
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7363-7375, 2022
Authors: Xu, Xinrui
Article Type: Research Article
Abstract: At present, with the continuous changes in the market situation and the continuous improvement of the supply chain network structure, the competition in all walks of life has become more and more intense, which has risen from simple enterprise competition to competition in the entire supply chain. In the construction industry, the structure of the construction supply chain is more complex and diverse, and it is more necessary to select high-quality suppliers for sincere cooperation. This requires construction companies to establish a complete supply chain management system, select high-quality suppliers to achieve win-win cooperation and improve their competitiveness. Therefore, construction …enterprises need to comprehensively consider various factors, build a reasonable and feasible evaluation index system according to their own demand for materials, and use appropriate evaluation methods to select material suppliers with specific advantages, so as to ensure the entire construction supply chain of the project. of smooth operation. In this paper, we introduced some calculating laws on interval-valued intuitionistic fuzzy sets (IVIFSs), Hamacher sum and Hamacher product and further propose the induced interval-valued intuitionistic fuzzy Hamacher ordered weighted average (I-IVIFHOWA) operator. Meanwhile, we also study some ideal properties of built operator. Then, we apply the I-IVIFHOWA operator to deal with the multiple attribute decision making (MADM) problems under IVIFSs. Finally, an example for selecting the building material suppliers is used to test this new approach. Show more
Keywords: Multiple attribute decision making (MADM), interval-valued intuitionistic fuzzy sets (IVIFSs), IOWA operator, I-IVIFHOWA operator, building material suppliers
DOI: 10.3233/JIFS-221001
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7377-7386, 2022
Authors: Baytürk, Engin | Küçükdeniz, Tarık | Esnaf, Şakir
Article Type: Research Article
Abstract: Location-routing problem (LRP) contains two Np-hard problems as, facility location (FL) and vehicle routing problem (VRP), in the same content. Since both problems directly affect the cost of distributions of the products and supply chain, the decision of location and routing is important for the success of companies. Therefore, many attempts are made to solve LRP problem in the literature. Researchers proposed exact and heuristic methods for LRP. However, exact methods cannot provide solutions for considerably large instances. In this paper, a new heuristic method is proposed for continuous or planar LRP. The proposed method contains fuzzy c-means for continuous …location problem and simulated annealing algorithm for vehicle routing problem, respectively. The proposed method is applied to both capacitated and uncapacitated LRP instances that are widely used in the literature. Results of the proposed method are compared with successful researches that are made on this problem in terms of the total cost. Show more
Keywords: Location-routing problem, simulated annealing algorithm, fuzzy c-means
DOI: 10.3233/JIFS-221168
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7387-7398, 2022
Authors: Jayasree, T. | Selvin Retna Raj, T.
Article Type: Research Article
Abstract: In this paper, the classification of power quality disturbances using combined ST/MST (S-Transform/Modified S-Transform) and Radial Basis Function Neural Network (RBFNN) is proposed. The extraction of significant features from the power quality disturbance signals is one of the challenging tasks in recognizing different disturbances. The Stockwell Transform/Modified Stockwell Transform (ST/MST) based features are distinct, understandable and more immune to noise. The important attributes present in the signals are retrieved from the ST/MST contours, MST 3D plots and MST based statistical curves. The relevant features are also extracted from the statistical curves. The extracted features are given as input to the …RBFNN for further classification. This method is evaluated under both noisy and noiseless conditions. The performance of the proposed approach is compared with other conventional approaches in the literature. The simulation results demonstrate that the proposed MST based RFNN technique is more effective for the detection and classification of power quality disturbances. Show more
DOI: 10.3233/JIFS-212399
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7399-7415, 2022
Authors: Du, Yuqin | Du, Xiangjun | Li, Yuanyuan | Hou, Fujun
Article Type: Research Article
Abstract: The aim of this paper is to introduce a Frank operator in the q-rung orthopair triangular fuzzy linguistic environment on the basis of the notion of the Frank operator and the q-rung orthopair fuzzy set. Firstly, the concept of a q-rung orthopair triangular fuzzy linguistic set (q-ROTrFLS) is proposed, then several basic operations, score, and accuracy functions to compare the q-ROTrFLS values are defined. Secondly, a series of q-rung orthopair triangular fuzzy linguistic Frank aggregation operators are developed, such as q-rung orthopair triangular fuzzy linguistic Frank weighted average (q-ROTrFLWA)operator,q-rung orthopair triangular fuzzy linguistic Frank weighted geometric (q-ROTrFLWG) operator, and we …introduce several relevant properties of these operators and prove their validity, and show the relevant relationship between some operators. Thirdly, two different decision-making approaches are constructed in the q-rung orthopair triangular fuzzy linguistic environment. Furthermore, a practical example is given to explain the developed methods. Finally, a comparative study is conducted, and the relevant sensitivity analysis is also discussed, and the outcome shows the prominence and the effectiveness of the developed methods compared to previous studies. Show more
Keywords: q-rung orthopair triangular fuzzy linguistic set, Frank operator, multi-attribute decision making (MADM), q-rung orthopair set
DOI: 10.3233/JIFS-220556
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7417-7445, 2022
Authors: Huang, Dan | Lin, Hai | Li, Zhaowen
Article Type: Research Article
Abstract: Information system (IS) is a significant model in the field of artificial intelligence. Information structure is not only a research direction in the field of granular computing (GrC), but also an important method to study an IS. A multiset-valued information system (MVIS) refers to an IS where information values are multisets. A MVIS can be seen as a model that is the result of information fusion of multiple categorical ISs. This model helps deal with missing values in the dataset. This paper studies information structures in a MVIS on the view of GrC and consider their application for uncertainty measurement …(UM). First of all, some notions of multisets and probability distribution sets (PDSs) are proposed. Naturally, relationships between multisets and PDSs are researched. Then, the concept of a MVIS based on the notion of multisets is given, and the internal structure of a MVIS is revealed by an incomplete information system (IIS). Furthermore, tolerance relations in a MVIS are defined by using Hellinger distance, and tolerance classes are obtained to construct the information structures of a MVIS. Considering the association of information structures, relationships between information structures are raised from the two aspects of dependence and separation. Moreover, some properties between information structures are provided by using information distance and inclusion degree. Finally, four UMs as the applications of information structures are investigated, and comprehensive experiments on several datasets demonstrate the feasibility and superiority of the proposed measures. These results will be helpful for establishing a framework of GrC in a MVIS and studying UM. Show more
Keywords: GrC, RST, Information fusion, PDS, MVIS, Information structure, UM
DOI: 10.3233/JIFS-220652
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7447-7469, 2022
Authors: Neelamegam, G. | Marikkannu, P.
Article Type: Research Article
Abstract: The Cloud-based storage is able to store more information in gigabyte size in all formats such as text, image or video and it can access at any time with their login credentials. In such a system, reducing the duplication of data and increasing security is an important factor for efficient storage. In this work, the file level de-duplication process is applied on the Magnetic Resonance Imaging (MRI) brain image by reducing the shares of the image to retrieve an original image from the cloud. To reduce the storage problem in this an optimization-based RSSS is used. The objective of this …investigation is to decrease the storage blow-up problem in Cloud storage and reduce the duplicate files in the Cloud storage of the health care centre. The proposed model comprises of two subsets: In the first set, the input image is divided into a number of shares using RSSS scheme. In the second set, the minimum share is determined by using the optimization process and it is encrypted and it is stored in the Cloud. Initially, the image is divided into number of shares for reconstructing using the ramp secret sharing scheme.Without these shares, the original image cannot be recovered. But storing all the shares result in high storage capacity. It is overcome with the help of Ant Lion optimization (ALO) to determine the minimum number of shares required for recovering the image. The ALO works to minimizing the Mean Square Error (MSE) of the image reconstruction to find the minimum shares. Then, the minimum shares are encrypted and converted into hash keys. Those hash keys are stored in the Cloud storage. The proposed ALO-RSSS is achieved its objective by reducing the shares to 2 as compared to the traditional method as well as the PSNR is 27% improved. Show more
Keywords: Cloud security, data de-duplication, ramp secret sharing scheme, ant lion optimization, shares, storage blow up
DOI: 10.3233/JIFS-212898
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7471-7484, 2022
Authors: Gnanaprakasam, C.N. | Brindha, G. | Gnanasoundharam, J. | Ahila Devi, E.
Article Type: Research Article
Abstract: In this paper proposes an efficient hybrid approach for resolve the issues based on unit commitment model integrated with electric vehicles considering the responsive load. The proposed hybrid approach is the combined performance of both the Multi-fidelity meta-optimization and Turbulent Flow of water based optimization (TFWO) and later it is known as MFM-TFWO method. The major objective of proposed approach is reduction of operational costs, reduction of real power losses, and reduction of emissions and improves the voltage stability index. The proposed system is incorporated with wind turbine and photovoltaic, electrical and thermal energy storage systems. The MFM approach is …performed for the optimization of the best combination of thermal unit depend on uncertainty; cost minimization, constraints of the system. For capturing the uncertainty and ensuring the demand satisfaction is performed by the TFWO approach. The proposed approach evaluates the impact of the stochastic behavior of electric vehicles and responsive load of the demand side management. The proposed method considers the uncertainty of PV, wind, thermal, electrical demands, and electric vehicles. At last, the proposed model is actualized in MATLAB/Simulink platform and the performance is compared with other techniques. The simulation results depicted that electric vehicles and responsive loads on energy management is decreasing the operation cost and emissions. Show more
Keywords: Operational costs, active power losses, emissions, voltage stability index, combined cooling heating and power, electric vehicle, responsive loads, energy storage
DOI: 10.3233/JIFS-220810
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7485-7510, 2022
Authors: Bai, Yuhang | Wang, Chunbo | Zhang, Lizhong
Article Type: Research Article
Abstract: With the continuous opening up of China’s dairy market to foreign countries, dairy products import volume continues to grow rapidly. The structural vector autoregressive model (SVAR) was used in this article to analyze the impact of dairy product imports on China’s raw milk production from 1996 to 2017. It is found that, dairy product import volume has a positive impact on China’s raw milk production, and negative impact on the liquid dairy product; and mainly negative impacts on the cost control variables in the short term. The price of corn has a stronger impact on the raw milk production compared …with that of the soybean meal prices and crude oil price; the impact of Domestic raw milk demand on raw milk production fluctuates frequently in the short term, and has a positive impact on the diary export. Based on this, this article believes that adjusting the milk industry policy, optimizing the dairy products import structure and the dairy cows’ source structure, and advocating scientific feeding can effectively alleviate the impact caused by dairy products import. Show more
Keywords: Dairy import, raw milk production, shock effect, Structure vector autoregressive model (SVAR)
DOI: 10.3233/JIFS-221220
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7511-7524, 2022
Authors: Anitha, R. | Bapu, B.R. Tapas
Article Type: Research Article
Abstract: In wireless sensor network (WSN), routing is one of the substantial maneuvers for distributing data packets to the base station. But malevolent node outbreaks will happen during routing process, which exaggerate the wireless sensor network operations. Therefore, a secure routing protocol is required, which safeguards the routing fortification and the wireless sensor network effectiveness. The existing routing protocol is dynamically volatile during real time instances, and it is very hard to recognize the unsecured routing node performances. In this manuscript, a Deep Dropout extreme Machine learning optimized Improved Alpha-Guided Grey Wolf based Crypto Hash Signature Token fostered Blockchain Technology is …proposed for secure dynamic optimal routing in Wireless Sensor Networks (SDOR-DEML-IAgGWO-CHS-BWSN). In this, Crypto Hash signature (CHS) token are generated for flow accesses with a secret key owned by each routing sensor node and it also offers an optimal path for data transmission. Then the secured dynamic optimal routing information is delivered through the proposed Blockchain based wireless sensor network platform with the help of Deep Dropout Extreme Machine learning optimized Improved Alpha-Guided Grey Wolf routing algorithm. Then the proposed method is simulated using the NS-2 (Network Simulator) tool. The simulation performance of the proposed SDOR-DEML-IAgGWO-CHS-BWSN method provide 76.26%, 65.57%, 60.85%, 48.99% and 42.9% lower delay during 30% malicious routing environment, 73.06%, 63.82%, 59.25%, 44.79% and 38.84% lower delay during 60% malicious routing environment is compared with the existing methods. Show more
Keywords: Wireless sensor network, secured routing protocol, malicious node attacks, Deep Dropout extreme machine learning, Improved Alpha-Guided Grey Wolf, Crypto Hash Signature token, blockchain technology
DOI: 10.3233/JIFS-212455
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7525-7543, 2022
Authors: Fang, Min | Liu, Lu | Ye, Yuxin | Zhu, Beibei | Han, Jiayu | Peng, Tao
Article Type: Research Article
Abstract: Knowledge graphs have been introduced into recommender systems due to the rich connectivity information. Many knowledge-aware recommendation methods use graph neural networks (GNNs) to capture the high-order structural and semantic information of knowledge graphs. However, previous GNN-based methods have the following limitations: (1) they fail to make full use of the neighborhood information of entities and (2) they ignore the importance of user interaction sequences on reflecting user preferences. As such, these models are insufficient for generating accurate representations of users and items. In this study, we propose a K nowledge-aware H ierarchical A ttention N etwork (KHAN) to provide …better recommendation. Specifically, the proposed model mainly consists of an item encoder and a user encoder. The item encoder is equipped with a hierarchical attention network, which is used to generate entity (item) representations by carefully aggregating neighborhood information of entities. The user encoder is also designed to learn more informative user representations from user interaction sequences using multi-head self-attention. The learned user representations are then combined with user representations introduced in the item encoder through a gating mechanism to generate the final user representations. Extensive experiments on two real-world datasets about movie and restaurant recommendation demonstrate the effectiveness of our model. Show more
Keywords: Recommender system, knowledge graph, graph neural network, hierarchical attention network
DOI: 10.3233/JIFS-212918
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7545-7557, 2022
Authors: Cai, Jinya | Zhang, Haiping | Yu, Xinping
Article Type: Research Article
Abstract: The modified bee colony algorithm is one of the excellent methods that has been proposed in recent years for data clustering. This MBCO algorithm randomly values the primary centers of the cluster by selecting a number of data from the data set, which makes the algorithm sensitive to the presence of noise and outgoing data in the data set and reduces its performance. Therefore, to solve this problem, the proposed method used three approaches to quantify the initial centers of the clusters. In the proposed method, first the initial centers of the clusters are generated by chaos methods, KMeans++algorithm and …KHM algorithm to determine the optimal position for the centers. Then the MBCO algorithm starts working with these centers. The performance of the proposed method compared to a number of other clustering methods was evaluated on 7 UCI datasets based on 6 clustering evaluation criteria. For example, in the iris data set, the proposed method with chaos approaches, KHM and KMeans++with accuracy of 0.8725, 0.8737 and 0.8725, respectively, and the MBCO method with accuracy of 0.8678, and in terms of CH criteria, the proposed method with chaotic approaches, KHM and KMeans++reached values of 0.3901, 0.54848, 0.5147 and MBCO method of 0.3620, respectively. Better achieved. In general, the results of the experiments according to the 6 evaluation criteria showed better performance of the proposed method compared to other methods in most data sets according to the 6 evaluation criteria. Show more
Keywords: Modified bee colony optimization, KMeans++algorithm, KHM algorithm, clustering
DOI: 10.3233/JIFS-220739
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7559-7575, 2022
Authors: Balasubramanian, C. | Lal Raja Singh, R.
Article Type: Research Article
Abstract: This paper proposes an efficient energy management approach for managing the demand response and energy forecasting in a smart grid using Internet of Things (IoT). The proposed energy management approach is the hybrid technique that is the joint execution of adaptive neuro fuzzy inference system (ANFIS) and balancing composite motion optimization (BCMO), thus it is called ANFIS-BCMO technique. An energy management approach is developed using price-based demand response (DR) program for IoT-enabled residential buildings. Then, we devised a approach depends on ANFIS-BCMO technique to systematically manage the energy use of smart devices in IoT-enabled residential buildings by programming to relieve …peak-to-average ratio (PAR), diminish electricity cost, and increase user comfort (UC). This maximizes effective energy utilization, which in turn increases the sustainability of IoT-enabled residential buildings on smart cities. The ANFIS-BCMO technique automatically responds to price-based DR programs to combat the main problem of DR programs that is the limitation of the consumer’s knowledge to respond when receiving DR signals. For consumers, the proposed ANFIS-BCMO based strategy programs appliances to exploit benefit based on reduced electricity bill. By then, the proposed method increases the stability of the electrical system by smoothing the demand curve. At last, the proposed model is executed on MATLAB/Simulink platform and the proposed method is compared with existing systems. Show more
Keywords: Energy management, demand response, energy forecast, smart grid, internet of things
DOI: 10.3233/JIFS-221040
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7577-7593, 2022
Authors: Yen, Chih-Ping
Article Type: Research Article
Abstract: We examine correlation coefficients for single-valued neutrosophic hesitant fuzzy sets (SVNHFSs) to point out their questionable results for the ideal alternative. Then, we propose three similarity measure methods to solve multi-criteria decision-making (MCDM) problems. Three applications, namely, ranking of alternatives, dysfunctional comments of turbine engine generators, and disease diagnoses for patients, illustrate the stability and effectivity of our new similarity. Our findings will help researchers deal with similarity measures in the future.
Keywords: Multiple criteria decision-making, correlation coefficient, single-valued neutrosophic hesitant fuzzy sets
DOI: 10.3233/JIFS-221142
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7595-7604, 2022
Authors: Sindhusaranya, B. | Geetha, M.R. | Rajesh, T. | Kavitha, M.R.
Article Type: Research Article
Abstract: Blood vessel segmentation of the retina has become a necessary step in automatic disease identification and planning treatment in the field of Ophthalmology. To identify the disease properly, both thick and thin blood vessels should be distinguished clearly. Diagnosis of disease would be simple and easier only when the blood vessels are segmented accurately. Existing blood vessel segmentation methods are not supporting well to overcome the poor accuracy and low generalization problems because of the complex blood vessel structure of the retina. In this study, a hybrid algorithm is proposed using binarization, exclusively for segmenting the vessels from a retina …image to enhance the exactness and specificity of segmentation of an image. The proposed algorithm extracts the advantages of pattern recognition techniques, such as Matched Filter (MF), Matched Filter with First-order Derivation of Gaussian (MF-FDOG), Multi-Scale Line Detector (MSLD) algorithms and developed as a hybrid algorithm. This algorithm is authenticated with the openly accessible dataset DRIVE. Using Python with OpenCV, the algorithm simulation results had attained an accurateness of 0.9602, a sensitivity of 0.6246, and a specificity of 0.9815 for the dataset. Simulation outcomes proved that the proposed hybrid algorithm accurately segments the blood vessels of the retina compared to the existing methodologies. Show more
Keywords: Hybrid algorithm, blood vessel segmentation, first-order derivation of Gaussian, matched filter, multi-scale line detector
DOI: 10.3233/JIFS-221137
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7605-7615, 2022
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